Figures for Paper
Dylan Glave
1/28/2022
R Markdown
Reading in Data
scaled_sum <- read.csv("scaled_sum.csv")
hhs_all_complete <- read_csv("hhs_all_complete.csv")## New names:
## * `` -> ...1## Rows: 19745 Columns: 237## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (81): submissionid, 44_meeting_attendance, 45_leadership_position, 48_...
## dbl (155): ...1, level4_id, lat, lon, ma_area, 9_region_member, 10_mpa_impo...
## dttm (1): updatedat##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.scaled_km_df <- read_csv("scaled_km_df.csv")## New names:
## * `` -> ...1## Rows: 4451 Columns: 130## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (37): submissionid, country, level1_name, level2_name, level4_name, ma_n...
## dbl (93): ...1, x1, level4_id, lat, lon, ma_area, x9_region_member, x44_meet...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.We want to include a point where all responses are “neutral” on our map of compliance behaviors
neutral_point <- scaled_sum %>%
filter(country == "NEUTRAL")1. Mapping Indices
We now have one (x,y) point for each fisher observation, and can map them onto an X-Y plane of Agreement and Engagement.
ggplot(data = scaled_sum,
aes(x = agree_mean, y = eng_mean)) +
geom_point(color = "darkblue", size = 0.7, alpha = 0.4) +
geom_point(data = neutral_point, aes(x = agree_mean, y = eng_mean),
size = 8, color = "pink2") +
labs( x = "Agreement (Scaled across 5 Questions)",
y = "Engagement (Scaled across 5 Questions)",
title = "Engagement and Agreement Index Mapping") +
# scale_color_viridis(discrete = TRUE, option = "D") +
# scale_x_continuous(limits = c(-3, 1.7),
# breaks = c(-2, -1, 0, 1, 1.5)) +
# scale_y_continuous(limits = c(-1.5, 1.5),
# breaks = c( -1, 0, 1, 1.5)) +
guides(colour = guide_legend(override.aes = list(size=7))) +
theme_bw() 
*Figure 1: A plot with the **neutral* fisher reported within the spread of our Z-score-based indices. We see that Ms. Neutral is 1 SD below the mean in agreement. This location means that the majority of fishers at least somewhat agree with rules in place. Meanwhile, we see dense grouping between -0.5 and 0.5 on both axes. Quite a few fishers have the maximum score for agreement, while a smaller number meet the ceiling of engagement.
Size of Clusters
cluster_size <- scaled_km_df %>%
count(cluster_no)
ggplot(data = cluster_size, aes(x = cluster_no, y = n, fill = cluster_no)) +
geom_bar(stat = "identity") +
labs(x = "Fisher Type",
y = "Total Fishers per Group",
fill = "",
title = "Sizes of Fisher-Types") +
scale_fill_viridis(discrete = T) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
geom_text(aes(label = n), size = 3, vjust=2, color = "white")
Between Cluster 1 and 4 (our “average” fishers,) we see that C4 is a full sd higher on question 43 “Do you think there is any benefit to regulating fishing via a managed access area and a reserve area?”
Both C1 and C4 (average fishers) have higher values in question x61g (willingness to change fishing behavior for the sake of conservation,) and they slightly disagree with “encouraging others to follow regulations.”
Average fishers aren’t engaging much (including meeting attendance.) From an NGO perspective, if one wants to shift fisher behavior+attitudes, then try and bring the reluctant folks into the governance process.
2. Compliance Behaviors of Admitted rule-breakers
counts_63 <- scaled_sum %>%
count(x63_fishing_in_reserve, country)
filtered_63 <- scaled_sum %>%
filter(x63_fishing_in_reserve == "1")
ggplot(data = filtered_63,
aes(x = agree_mean, y = eng_mean)) +
geom_point(color = "darkblue", size = 1.2, alpha = 1,
show.legend = TRUE) +
labs( x = "Agreement (Scaled across 5 Questions)",
y = "Engagement (Scaled across 5 Questions)",
title = "Engange and Agreement of Admitted rule-breakers") +
scale_x_continuous(limits = c(-3, 2),
breaks = c(-2, -1, 0, 1, 2)) +
scale_y_continuous(limits = c(-1.5, 2),
breaks = c( -1, 0, 1, 2)) +
theme_bw() 
Figure 4: A graph of Agreement and Engagement measures, colored by responses of “Do you fish inside the reserve?” These fishers range
Question 63 asks if fishers break the rules, and fish in the reserve. This would be a classic measure of compliance (although self-reporting likely leads to a lower N of admitted non-compliers)
138 fishers admitted that they fish in the reserve, while 2800 said they do not. The question we’re left with is: What is different about folks who admit to breaking rules? These “Yes” rule-breakers are spread relatively evenly across all countries. Maybe they don’t think there are sanctions, their community doesn’t care about it
I’ll do the same “rule-breaker” analysis, but against the question, “Out of 10 people in your community, how many people…Fished in the reserve in the last month”
3. Full Cluster Map
Here, I’ve attached the summary statistics of the Clusters. We ahve them on hand for tables, individual stats, Chi-Squared tests, etc.
cluster_summary_df <- scaled_km_df %>%
group_by(cluster_no) %>%
summarise(eng_mean = mean(eng_mean),
agree_mean = mean(agree_mean),
x44_mean = mean(x44_meeting_attendance),
x45_mean = mean(x45_leadership_position),
x48_mean = mean(x48_enforcement_participation),
x53_mean = mean(x53_encourage_regulations),
x61g_mean = mean(x61g_fishing_change_behavior),
x43_mean = mean(x43_ma_benefits),
x46_mean = mean(x46_represent_interests),
x52_mean = mean(x52_ma_benefit_5yrs),
x61a_mean = mean(x61a_current_regulations),
x61f_mean = mean(x61f_rights_distribution_fair),
x30a_mean = mean(x30_trust_local_decision),
x44_median = median(x44_meeting_attendance),
x45_median = median(x45_leadership_position),
x48_median = median(x48_enforcement_participation),
x53_median = median(x53_encourage_regulations),
x61g_median = median(x61g_fishing_change_behavior),
x43_median = median(x43_ma_benefits),
x46_median = median(x46_represent_interests),
x52_median = median(x52_ma_benefit_5yrs),
x61a_median = median(x61a_current_regulations),
x61f_median = median(x61f_rights_distribution_fair),
x30a_median = median(x30_trust_local_decision)) %>%
mutate(across(where(is.numeric), ~ round(., 2)))
## Here, the last line of code rounds to the second decimal
cluster_medians <- cluster_summary_df %>%
select(1, 14:23)
cl_mean_graphable_df <- cluster_summary_df %>%
select(eng_mean, agree_mean, cluster_no)
scaled_cluster_counts <- scaled_km_df %>%
count(cluster_no)
ggplot() +
geom_point(data = scaled_km_df,
aes(x = agree_mean, y = eng_mean,
color = cluster_no),
size = 0.7, alpha = 0.7) +
geom_point(data = cl_mean_graphable_df,
aes(x = agree_mean, y = eng_mean,
color = cluster_no),
size = 6, shape = "triangle", show.legend = FALSE) +
labs( x = "Agreement (Scaled across 5 Questions)",
y = "Engagement (Scaled across 5 Questions)",
title = "Mapping Clusters",
color = "Cluster") +
scale_x_continuous(limits = c(-3, 2),
breaks = c(-2, -1, 0, 1, 2)) +
scale_y_continuous(limits = c(-1.5, 2),
breaks = c( -1, 0, 1, 2)) +
guides(colour = guide_legend(override.aes = list(size=7)))## Warning: Removed 10 rows containing missing values (geom_point).
The 4 Clusters, done marked by color. Means are marked by triangles.
Between Cluster 1 and 4 (our “average” fishers,) we see that C4 is a full sd higher on question 43 “Do you think there is any benefit to regulating fishing via a managed access area and a reserve area?”
Both C1 and C4 (average fishers) have higher values in question x61g (willingness to change fishing behavior for the sake of conservation,) and they slightly disagree with “encouraging others to follow regulations.”
Average fishers aren’t engaging much (including meeting attendance.) From an NGO perspective, if one wants to shift fisher behavior+attitudes, then try and bring the reluctant folks into the governance process.
kmeans_kable <- head(cluster_summary_df) %>%
kable( format = "html", caption = "Summary of Clusters") %>%
kable_styling(bootstrap_options = "striped",
full_width = F,
position = "left") %>%
collapse_rows(columns = 3, valign = "middle")
kmeans_kable| cluster_no | eng_mean | agree_mean | x44_mean | x45_mean | x48_mean | x53_mean | x61g_mean | x43_mean | x46_mean | x52_mean | x61a_mean | x61f_mean | x30a_mean | x44_median | x45_median | x48_median | x53_median | x61g_median | x43_median | x46_median | x52_median | x61a_median | x61f_median | x30a_median |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Committed | 0.87 | 0.30 | 0.89 | 0.52 | 0.50 | 0.66 | 4.19 | 0.65 | 0.89 | 0.72 | 4.06 | 4.10 | 4.10 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 4 | 4 | 4 |
| Resistant | -0.42 | -0.87 | -0.47 | -0.65 | -0.74 | -0.45 | 3.38 | 0.46 | 0.01 | 0.19 | 2.76 | 2.80 | 2.74 | -1 | -1 | -1 | 0 | 4 | 0 | 0 | 0 | 3 | 3 | 3 |
| Supportive | -0.18 | 0.31 | -0.06 | -0.81 | -0.86 | -0.15 | 4.07 | 1.00 | 0.76 | 0.66 | 4.10 | 3.95 | 3.90 | -1 | -1 | -1 | 0 | 4 | 1 | 1 | 1 | 4 | 4 | 4 |
| Wavering | -0.24 | -0.13 | -0.19 | -0.86 | -0.87 | -0.18 | 4.03 | 0.00 | 0.70 | 0.30 | 4.13 | 3.87 | 4.04 | -1 | -1 | -1 | 0 | 4 | 0 | 1 | 0 | 4 | 4 | 4 |
4. Perceived Catch + Clusters
no_na_19 <- scaled_km_df %>%
drop_na(x19_current_fish_catch) %>%
mutate(x19_current_fish_catch = recode_factor(x19_current_fish_catch,
"Declined a lot" = "1. Declined a lot",
"Declined slightly" = "2. Declined slightly",
"Stayed the same" = "3. Stayed the same",
"Improved slightly" = "4. Improved slightly",
"Improved heavily" = "5. Improved heavily"))
mean_19_df <- no_na_19 %>%
group_by(x19_current_fish_catch) %>%
summarise(eng_mean = mean(eng_mean),
agree_mean = mean(agree_mean)) x19_counts <- no_na_19 %>%
count(x19_current_fish_catch)
x19_cluster_counts <- no_na_19 %>%
group_by(cluster_no) %>%
count(x19_current_fish_catch)
ggplot(x19_cluster_counts,
aes(x = x19_current_fish_catch, y = n, fill = cluster_no,
)) +
geom_bar(position="fill", stat="identity") +
labs( x = "Perception of Fish Stock (over last 2 years)",
y = "Percentage",
fill = "Cluster") +
guides(colour = guide_legend(override.aes = list(size=7))) +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
reversed_x19_counts <-
no_na_19 %>%
group_by(x19_current_fish_catch) %>%
count(cluster_no)
ggplot(reversed_x19_counts,
aes(x = cluster_no, y = n, fill = x19_current_fish_catch)) +
geom_bar(position="fill", stat="identity", alpha = 0.7) +
labs( x = "Clusters",
y = "Percentage of Cluster",
title = "Perceived Catch and Clusters",
fill = "Perceived Change in Fish Catch \n (compared with 2 years prior)") +
scale_fill_viridis(discrete = T) +
theme_bw()
5. Fishing Effort in “Low Season”
First, I clean the dataframe
fishing_effort_17 <- scaled_km_df %>%
filter(x17_fishing_low_profit %in% c("Once or never",
"A few times",
"A few times per month",
"1-2 per week",
"More than 1-2 times per week",
na.rm = FALSE)) %>%
drop_na(x17_fishing_low_profit) %>%
mutate(x17_fishing_low_profit = recode_factor(x17_fishing_low_profit, "Once or never" = "Rarely or Never")) %>%
mutate(x17_fishing_low_profit = recode_factor(x17_fishing_low_profit, "A few times" = "Rarely or Never"))
x17_counts <- fishing_effort_17 %>%
group_by(x17_fishing_low_profit) %>%
count()
x17_cluster_counts <- fishing_effort_17 %>%
group_by(cluster_no) %>%
count(x17_fishing_low_profit)Then I visualize
ggplot(x17_cluster_counts,
aes(x = cluster_no, y = n, fill = x17_fishing_low_profit)) +
geom_bar(position="fill", stat="identity", alpha = 0.8) +
labs( x = "Fisher Type",
y = "Percentage of Group",
title = "Fishing Effort in Offseason",
fill = "Fishing Effort (Per Week)") +
scale_fill_viridis(discrete = T) +
scale_y_continuous(labels = scales::percent) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
- Resistant fishers who fish 1+ times per week: 236/526 = 45%
- Wavering fishers who fisher 1+ times per week: 257/998 = 26%
- Supportive fishers who fisher 1+ times per week: 370/1279 = 29%
- Committed fishers who fisher 1+ times per week: 236/812 = 29%
Second figure will be another stacked barplot This is rad! I’m going to code away at x18, and then do a little more interpreting.
6. Fishing Effort in “High Season”
Dataframe cleaning
x18_fishing_effort <- scaled_km_df %>%
filter(x18_fishing_high_profit %in% c(
"Once or never",
"A few times",
"A few times per month",
"1-2 per week",
"More than 1-2 times per week")) %>%
mutate(x18_fishing_high_profit = recode_factor(x18_fishing_high_profit, "Once or never" = "Rarely or Never")) %>%
mutate(x18_fishing_high_profit = recode_factor(x18_fishing_high_profit, "A few times" = "Rarely or Never")) %>%
drop_na(x18_fishing_high_profit) %>%
mutate(x18_fishing_high_profit = fct_relevel(x18_fishing_high_profit,
"Rarely or Never",
"A few times per month",
"1-2 per week",
"More than 1-2 times per week"))
# basic counts
x18_counts <- x18_fishing_effort %>%
count(x18_fishing_high_profit)
x18_cluster_counts <- x18_fishing_effort %>%
group_by(cluster_no) %>%
count(x18_fishing_high_profit)Let’s do the same visualization for 18, as I did for 17.
Visualziation
ggplot(x18_cluster_counts,
aes(x = cluster_no, y = n, fill = x18_fishing_high_profit)) +
geom_bar(position="fill", stat="identity", alpha = 0.8) +
labs( x = "Fisher Type",
y = "Percentage of Group",
title = "Fishing in High Season",
fill = "Frequency of Fishing") +
scale_fill_viridis(discrete = T) +
scale_y_continuous(labels = scales::percent) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
- Resistant fishers who fish 1+ times per week: 70%
- Wavering fishers who fisher 1+ times per week: 84%
- Supportive fishers who fisher 1+ times per week: 83%
- Committed fishers who fisher 1+ times per week: 73%
supportive : 719, wavering 563, Committed 414, resistant 227
7. Food Security
- Question #23: “Do you believe that the job of a fisher is secure in the future?”
- Question #29: “To cover family needs your household income is…”
- Question #54: “How do you rate the last year in terms of food availability?”
- Question #59: “Are you confident that you will be able to procure enough food for you and your family for the next 12 months?”
- Question #60: In the last 12 months, how often did your household eat fish?
Starting with 55 and 56
food_security <- scaled_km_df %>%
select(country, cluster_no, x23_job_secure, x26_fishing_income_save, x28_buyer_loans, x29_family_income, x55_worry_food, x59_food_procurement, x60_hh_fish_consumption, eng_mean, agree_mean, x54_food_availability) %>%
drop_na()Ability to cover family costs
income_counts <- food_security %>%
mutate(x29_family_income = fct_relevel(x29_family_income,
"Sufficient",
"Tight",
"Insufficient")) %>%
group_by(cluster_no) %>%
count(x29_family_income)
ggplot(income_counts,
aes(x = cluster_no, y = n, fill = x29_family_income)) +
geom_bar(position="fill", stat="identity", alpha = 0.8) +
labs( x = "Clusters (1 and 4 are 'average')",
y = "Percentage of Cluster",
title = "Ability to cover family costs",
fill = "Household funds are..") +
scale_fill_viridis(option="inferno", discrete = T) +
scale_y_continuous(labels = scales::percent) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
Below, Food Security is asked in terms of the quality of food availability the past year, and the confidence for the upcoming year.
Measuring Question 54, “How do you rate the last year in terms of food availability?”
x54_cluster_counts <- food_security %>%
group_by(cluster_no) %>%
count(x54_food_availability) %>%
drop_na()
ggplot(x54_cluster_counts,
aes(x = cluster_no, y = n, fill = x54_food_availability)) +
geom_bar(stat="identity", alpha = 0.8) +
labs( x = "Clusters (1 and 4 are 'average')",
y = "Total # of Fishers",
title = "Food Availability (Past Year)",
fill = "'Rating' Food availability",
caption = "How do you rate the last year in terms of food availability?") 
ggplot(x54_cluster_counts,
aes(x = cluster_no, y = n, fill = x54_food_availability)) +
geom_bar(position="fill", stat="identity", alpha = 0.7) +
labs( x = "Fisher Type",
y = "Percentage of fishers",
title = "Food Availability (past Year)",
fill = "Level of Food") +
scale_fill_viridis(discrete = T)
“Are you confident that you will be able to procure enough food for you and your family for the next 12 months?
x59_counts <- food_security %>%
group_by(cluster_no) %>%
count(x59_food_procurement) %>%
drop_na() %>%
mutate(x59_food_procurement = recode_factor(x59_food_procurement,
"Confident not" = "Not confident",
"Very confident not" = "Very not confident"))
ggplot(x59_counts,
aes(x = cluster_no, y = n, fill = x59_food_procurement)) +
geom_bar(stat="identity", alpha = 0.8) +
labs( x = "Clusters (1 and 4 are 'average')",
y = "Total # of Fishers",
title = "Food Security Confidence (Upcoming Year)",
fill = "'Rating' Food availability",
caption = "Are you confident that you will be able to procure enough food for you and your family for the next 12 months?") +
# geom_text(aes(label = n),position = position_dodge(0.9), vjust = 2, size = 2, color = "gray1")+
theme_bw()
Food Security (“worrying about food”)
x55_cluster_counts <- food_security %>%
mutate(x55_worry_food = fct_relevel(x55_worry_food,
c("Never",
"Sometimes",
"Often"))) %>%
group_by(x55_worry_food) %>%
count(cluster_no)
ggplot(x55_cluster_counts,
aes(x = cluster_no, y = n, fill = x55_worry_food)) +
geom_bar(position="fill", stat="identity", alpha = 0.7) +
labs( x = "Fisher Type",
y = "Percentage of fishers",
title = "Food Insecurity",
fill = "Frequency \n of Worry",
caption = "Worry about Food Security (last 12 months)") +
scale_fill_viridis(option="inferno", discrete = T) +
scale_y_continuous(labels = scales::percent) +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
theme_bw()
#8. Enforcement Type + Clusters
Starting with Enforcement
enforcement_df <- scaled_km_df %>%
select(eng_mean, agree_mean, cluster_no, x49_enforcement_responsible, x51b_fishers_reserves, country) %>%
filter(x49_enforcement_responsible %in% c("Fisheries Management Body",
"Subnational Government",
"No enforcement system",
"Myself",
"National Government", na.rm = FALSE))
### To make a grouped bar chart, I want counts for each cluster
enforcement_counts <- enforcement_df %>%
select(eng_mean, agree_mean, cluster_no, x49_enforcement_responsible, x51b_fishers_reserves, country) %>% mutate(x49_enforcement_responsible = fct_relevel(x49_enforcement_responsible, "Fisheries Management Body",
"No enforcement system",
"Myself",
"Subnational Government",
"National Government")) %>%
group_by(x49_enforcement_responsible) %>%
count(cluster_no)
swtiched_enforcement_counts <- enforcement_df %>%
mutate(x49_enforcement_responsible = fct_relevel(x49_enforcement_responsible, "Fisheries Management Body",
"No enforcement system",
"Myself",
"Subnational Government",
"National Government")) %>%
group_by(cluster_no) %>%
count(x49_enforcement_responsible)
### Above = Data cleaning
ggplot(swtiched_enforcement_counts,
aes(x = cluster_no, y = n, fill = x49_enforcement_responsible)) +
geom_bar(position="fill", stat="identity", alpha = 0.8) +
labs( x = "Cluster #",
y = "Proportion",
title = "Enforcement By Cluster",
fill = "Enforcement Type") +
guides(colour = guide_legend(override.aes = list(size=7))) +
theme_bw() +
scale_y_continuous(labels = scales::percent) +
theme(axis.text.x = element_text(angle = 45, hjust=1))
# scale_x_continuous(limits = c(-3, 2),
# breaks = c(-2, -1, 0, 1, 2)) +
# scale_y_continuous(limits = c(-1.5, 2),
# breaks = c( -1, 0, 1, 2)) +
ggplot(enforcement_counts,
aes(x = x49_enforcement_responsible, y = n, fill = cluster_no)) +
geom_bar(position="fill", stat="identity", alpha = 0.8) +
labs( x = "Enforcement Responsibility",
y = "Cluster",
title = "Enforcement By Cluster",
color = "Cluster") +
guides(colour = guide_legend(override.aes = list(size=7))) +
theme(axis.text.x = element_text(angle = 45, hjust=1))
“Who is responsible for enforcement in your community? 1. Fisheries Management Body 2. National Government 3. Subnational Government 4. Other (please state) 5. There is no enforcement system”
9. Job Security
job_security_counts <- food_security %>%
mutate(x23_job_secure = recode_factor(x23_job_secure, "1" = "Secure",
"0" = "Not Secure")) %>%
group_by(x23_job_secure) %>%
count(cluster_no) Wavering: 55% job secure Resistant: 46% job secure Committed: 71% job Secure Supportive: 65% job secure
Let’s graph this
ggplot(job_security_counts,
aes(x = cluster_no, y = n, fill = x23_job_secure)) +
geom_bar(position="fill", stat="identity", alpha = 0.7) +
labs( x = "Fisher Type",
y = "Total Fishers",
title = "Job Security",
color = "Cluster",
caption = "Perception of Job Security, 'in the future'",
fill = "") +
scale_fill_viridis(discrete = T) +
theme_bw() +
coord_flip()
## Second graph with ) at the midline, like a population chartComparing enforcement with Perceived Fish catch (Q19)
enforcement_and_catch <- scaled_km_df %>%
select(cluster_no, x19_current_fish_catch, x49_enforcement_responsible) %>%
filter(x49_enforcement_responsible %in% c("Fisheries Management Body",
"National Government",
"Subnational Government",
"No enforcement system",
"Myself")) %>%
drop_na()
x19_over_49 <- enforcement_and_catch %>%
group_by(x49_enforcement_responsible) %>%
count(x19_current_fish_catch)
x49_over_19 <- enforcement_and_catch %>%
group_by(x19_current_fish_catch) %>%
count(x49_enforcement_responsible)I’ll visualize the two, and verbalize + write what what each ones tells us (i.e. what’s different between the two.)
ggplot(x19_over_49,
aes(x = x19_current_fish_catch, y = n, fill = x49_enforcement_responsible)) +
geom_bar(position="dodge", stat="identity", alpha = 0.8) +
labs( x = "Enforcement Responsibility",
y = "N",
title = "Enforcement Against Perceived Catch",
fill = "Enforcement Responsibility") +
geom_text(aes(label = n),position = position_dodge(0.9), vjust = 1, size = 2, color = "gray1") +
guides(colour = guide_legend(override.aes = list(size=7))) +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
scale_fill_viridis(discrete = T) 
### Switching Axes
ggplot(x49_over_19,
aes(x = x49_enforcement_responsible, y = n, fill = x19_current_fish_catch)) +
geom_bar(position="fill", stat="identity", alpha = 0.8) +
labs( x = "Enforcement Responsibility",
y = "N",
title = "Enforcement Against Perceived Catch",
fill = "Perceived Catch Change") +
geom_text(aes(label = n),position = position_fill(0.9), vjust = 1, size = 2, color = "gray1") +
guides(colour = guide_legend(override.aes = list(size=7))) +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
scale_fill_viridis(discrete = T) 
What do we see?
When the X bins are Enforcement type, I think we see a better picture. Asking “how many folks, udner subnational enforcement saw no change if fish catch?” is easier than “OK, among those who saw a slight decline, how many have no enforcement system?”
We start with the institution (albeit perceived) and then step into the personal perception fo fish catch over time.
- FMB-responsibility has the highest amount of “improved (relative and absolute)
- Subnational ahd the msot “Stayed the Same” (relative)
- No enforcement or “myself” had the higher proportions of “Declined a lot.”
Let’s get some count data (classic post-visualization move)
enforcement_wider <- enforcement_counts %>%
pivot_wider(names_from = x49_enforcement_responsible,
values_from = n) %>%
bind_rows(summarise_all(., ~if(is.numeric(.)) sum(.) else "Total"))
enforcement_kable_1 <- head(enforcement_wider) %>%
kable( format = "html", caption = "Enforcement") %>%
kable_styling(bootstrap_options = "striped",
full_width = F,
position = "left") %>%
collapse_rows(columns = 3, valign = "middle")
enforcement_kable_1| cluster_no | Fisheries Management Body | No enforcement system | Myself | Subnational Government | National Government |
|---|---|---|---|---|---|
| Committed | 633 | 4 | 22 | 123 | 192 |
| Resistant | 276 | 106 | 27 | 107 | 150 |
| Supportive | 681 | 51 | 46 | 435 | 277 |
| Wavering | 499 | 33 | 23 | 361 | 199 |
| Total | 2089 | 194 | 118 | 1026 | 818 |
Chi-Squared Means Testing
**For the Figures that we include in the Manuscript, we’ll need to test the significance of the different groups. We have very large Ns and feel confident in the groupings for that reason.
2a. Enforcement Responsibility
Chi-Squared Test Null: There is no significant difference between the Cluster makeup of each bucket of enforcement responsibility Alternative: there is a menaingful difference between the compliance behaviors of each enforcement responsoibility
enforcement_counts_table <- enforcement_counts %>%
pivot_wider(names_from = x49_enforcement_responsible, values_from = n) %>%
rename(Cluster = cluster_no)
enforcement_counts_prop <- enforcement_counts_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
enforcement_counts_prop## Cluster Fisheries Management Body No enforcement system Myself
## Committed 633 (65%) 4 (0%) 22 (2%)
## Resistant 276 (41%) 106 (16%) 27 (4%)
## Supportive 681 (46%) 51 (3%) 46 (3%)
## Wavering 499 (45%) 33 (3%) 23 (2%)
## Subnational Government National Government
## 123 (13%) 192 (20%)
## 107 (16%) 150 (23%)
## 435 (29%) 277 (19%)
## 361 (32%) 199 (18%)kable(enforcement_counts_prop) %>%
kable_styling()| Cluster | Fisheries Management Body | No enforcement system | Myself | Subnational Government | National Government |
|---|---|---|---|---|---|
| Committed | 633 (65%) | 4 (0%) | 22 (2%) | 123 (13%) | 192 (20%) |
| Resistant | 276 (41%) | 106 (16%) | 27 (4%) | 107 (16%) | 150 (23%) |
| Supportive | 681 (46%) | 51 (3%) | 46 (3%) | 435 (29%) | 277 (19%) |
| Wavering | 499 (45%) | 33 (3%) | 23 (2%) | 361 (32%) | 199 (18%) |
Now let’s run the Chi-Squared test.
enforcement_pre_chi <- enforcement_counts_table %>%
select(-Cluster)
enforcement_chi <- chisq.test(enforcement_pre_chi)
enforcement_chi##
## Pearson's Chi-squared test
##
## data: enforcement_pre_chi
## X-squared = 431.86, df = 12, p-value < 2.2e-16Clustered Enforcement has a p-value < 2.2e-16
2b. Perceived Catch
perceived_catch_table <- x19_cluster_counts %>%
pivot_wider(values_from = n, names_from = x19_current_fish_catch) %>%
rename(Cluster = cluster_no)
preceived_catch_props <- perceived_catch_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
kable(preceived_catch_props) %>%
kable_styling()| Cluster |
|
|
|
|
|
|---|---|---|---|---|---|
| Committed | 200 (21%) | 422 (43%) | 186 (19%) | 151 (16%) | 14 (1%) |
| Resistant | 187 (26%) | 350 (48%) | 115 (16%) | 70 (10%) | 1 (0%) |
| Supportive | 322 (21%) | 632 (42%) | 447 (30%) | 95 (6%) | 9 (1%) |
| Wavering | 160 (15%) | 452 (41%) | 423 (39%) | 56 (5%) | 3 (0%) |
Now let’s do the Chi-squared for Perceived Catch and Clusters
pre_chi_perceived_catch <- perceived_catch_table %>%
ungroup() %>%
select(2:6)
chi_perceived_catch <- chisq.test(pre_chi_perceived_catch)## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrectchi_perceived_catch##
## Pearson's Chi-squared test
##
## data: pre_chi_perceived_catch
## X-squared = 242.96, df = 12, p-value < 2.2e-16Clusters and Perceived catch have a p-value < 2.2e-16
2c. Fishing Effort in Offseason
offseason_effort_table <- x17_cluster_counts %>%
pivot_wider(values_from = n, names_from = x17_fishing_low_profit) %>%
rename(Cluster = cluster_no)
##First I pivot_wider, and made a proportion table, using janitor and kableExtra
offseason_props <- offseason_effort_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
kable(offseason_props) %>%
kable_styling()| Cluster | Rarely or Never | A few times per month | 1-2 per week | More than 1-2 times per week |
|---|---|---|---|---|
| Committed | 416 (51%) | 160 (20%) | 194 (24%) | 42 (5%) |
| Resistant | 202 (38%) | 88 (17%) | 198 (38%) | 38 (7%) |
| Supportive | 735 (57%) | 174 (14%) | 333 (26%) | 37 (3%) |
| Wavering | 668 (67%) | 73 (7%) | 228 (23%) | 29 (3%) |
Actual Chi-Squared for offseason (x17)
pre_chi_offseason_effort <- offseason_effort_table %>%
ungroup() %>%
select(2:5)
chi_offseason <- chisq.test(pre_chi_offseason_effort)
chi_offseason##
## Pearson's Chi-squared test
##
## data: pre_chi_offseason_effort
## X-squared = 164.03, df = 9, p-value < 2.2e-16X-squared = 164.03, df = 9, p-value < 2.2e-16
2d. Fishing in High Season
high_season_table <- x18_cluster_counts %>%
pivot_wider(values_from = n, names_from = x18_fishing_high_profit) %>%
rename(Cluster = cluster_no)
##First I pivot_wider, and made a proportion table, using janitor and kableExtra
high_season_props <- high_season_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
kable(high_season_props) %>%
kable_styling()| Cluster | Rarely or Never | A few times per month | 1-2 per week | More than 1-2 times per week |
|---|---|---|---|---|
| Committed | 54 (8%) | 127 (19%) | 85 (12%) | 414 (61%) |
| Resistant | 44 (10%) | 88 (20%) | 89 (20%) | 227 (51%) |
| Supportive | 96 (9%) | 95 (9%) | 185 (17%) | 719 (66%) |
| Wavering | 66 (7%) | 78 (9%) | 199 (22%) | 563 (62%) |
Actual Chi-Squared for high season (x18)
pre_chi_high_season <- high_season_table %>%
ungroup() %>%
select(2:5)
chi_high_season <- chisq.test(pre_chi_high_season)
chi_high_season##
## Pearson's Chi-squared test
##
## data: pre_chi_high_season
## X-squared = 98.902, df = 9, p-value < 2.2e-16X-squared = 98.902, df = 9, p-value < 2.2e-16
2E. Family Income
“How would you rate household funds?”
income_29_table <- income_counts %>%
pivot_wider(values_from = n, names_from = x29_family_income) %>%
rename(Cluster = cluster_no)
##First I pivot_wider, and made a proportion table, using janitor and kableExtra
income_29_props <- income_29_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
kable(income_29_props) %>%
kable_styling()| Cluster | Sufficient | Tight | Insufficient |
|---|---|---|---|
| Committed | 196 (22%) | 462 (51%) | 242 (27%) |
| Resistant | 139 (20%) | 397 (58%) | 147 (22%) |
| Supportive | 179 (13%) | 926 (65%) | 316 (22%) |
| Wavering | 121 (11%) | 705 (67%) | 228 (22%) |
Actual Chi-Squared for family income (x29)
pre_chi_income_29 <- income_29_table %>%
ungroup() %>%
select(2:4)
chi_income_29 <- chisq.test(pre_chi_income_29)
chi_income_29##
## Pearson's Chi-squared test
##
## data: pre_chi_income_29
## X-squared = 83.72, df = 6, p-value = 6.078e-16X-squared = 83.72, df = 6, p-value = 6.078e-16
2F. Food Insecurity
“I worry about not having enough food for everyone in the household…”
x55_table <- x55_cluster_counts %>%
pivot_wider(values_from = n, names_from = x55_worry_food) %>%
rename(Cluster = cluster_no)
##First I pivot_wider, and made a proportion table, using janitor and kableExtra
x55_props <- x55_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
kable(x55_props) %>%
kable_styling()| Cluster | Never | Sometimes | Often |
|---|---|---|---|
| Committed | 162 (18%) | 482 (54%) | 256 (28%) |
| Resistant | 114 (17%) | 420 (61%) | 149 (22%) |
| Supportive | 268 (19%) | 892 (63%) | 261 (18%) |
| Wavering | 143 (14%) | 780 (74%) | 131 (12%) |
Actual Chi-Squared for food insecurty (x55)
pre_chi_x55 <- x55_table %>%
ungroup() %>%
select(2:4)
chi_x55 <- chisq.test(pre_chi_x55)
chi_x55##
## Pearson's Chi-squared test
##
## data: pre_chi_x55
## X-squared = 109.96, df = 6, p-value < 2.2e-16X-squared = 109.96, df = 6, p-value < 2.2e-16
2G.Job Security
“How secure is the job of a fisher in the future?”
x23_table <- job_security_counts %>%
pivot_wider(values_from = n, names_from = x23_job_secure) %>%
rename(Cluster = cluster_no)
##First I pivot_wider, and made a proportion table, using janitor and kableExtra
x23_props <- x23_table %>%
janitor::adorn_percentages(denominator = "row") %>%
adorn_pct_formatting(digits = 0) %>%
adorn_ns(position = "front")
kable(x23_props) %>%
kable_styling()| Cluster | Secure | Not Secure |
|---|---|---|
| Committed | 642 (71%) | 258 (29%) |
| Resistant | 316 (46%) | 367 (54%) |
| Supportive | 936 (66%) | 485 (34%) |
| Wavering | 590 (56%) | 464 (44%) |
Actual Chi-Squared for Job Security (Q. 23)
pre_chi_x23 <- x23_table %>%
ungroup() %>%
select(2:3)
chi_x23 <- chisq.test(pre_chi_x23)
chi_x23##
## Pearson's Chi-squared test
##
## data: pre_chi_x23
## X-squared = 128.23, df = 3, p-value < 2.2e-16X-squared = 128.23, df = 3, p-value < 2.2e-16
Section 3. Radar Plots
clusters_for_radar <- cluster_summary_df %>%
select(4:14)
rownames(clusters_for_radar) <- c("Committed", "Resistant", "Supportive", "Wavering")## Warning: Setting row names on a tibble is deprecated.max_min <- data.frame(
x44_mean = c(1, -1),
x45_mean = c (1, -1),
x48_mean = c(1, -1),
x53_mean = c (1, -1),
x61g_mean = c(5, 1),
x43_mean = c(1, 0),
x46_mean = c(1, 0),
x52_mean = c(2, -2),
x61a_mean = c(5, 1),
x61f_mean = c(5, 1),
x30a_mean = c(5, 1))
rownames(max_min) <- c("Max", "Min")
radar_df <- rbind(max_min, clusters_for_radar)
committed_radar <-radar_df[c("Max", "Min", "Committed"), ]
radarchart(committed_radar)
resistant_radar <- radar_df[c("Max", "Min", "Resistant"), ]
radarchart(resistant_radar)
supportive_radar <- radar_df[c("Max", "Min", "Supportive"), ]
radarchart(supportive_radar)
wavering_radar <- radar_df[c("Max", "Min", "Wavering"), ]
radarchart(wavering_radar)
# create_beautiful_radarchart(
# data = wavering_radar,
# color = "#00AFBB")